Abstract
Customer reviews mining can urge manufacturers to improve product quality and guide people a rational consumption. The commonly used mining methods are not satisfactory in precision of the features and opinions extracting. In this paper, we extracted the product features and opinion words in a unified process with semi-supervised learning algorithm, and made an adjustment of the threshold value of confidence to obtain a better mining performance, then adjusted the features sequence with big standard deviation, and maximized the harmonic-mean to raise the precision while ensured the recall. The experiment results show that our techniques are very effective.
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Huang, Y., He, Z., Wang, H. (2009). Optimization of Feature-Opinion Pairs in Chinese Customer Reviews. In: Chien, BC., Hong, TP., Chen, SM., Ali, M. (eds) Next-Generation Applied Intelligence. IEA/AIE 2009. Lecture Notes in Computer Science(), vol 5579. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02568-6_76
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DOI: https://doi.org/10.1007/978-3-642-02568-6_76
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02567-9
Online ISBN: 978-3-642-02568-6
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